Data-driven prediction of daily Cryptosporidium river concentrations for water resource management: Use of catchment-averaged vs spatially distributed features in a Bagging-XGBoost model.
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| Title: | Data-driven prediction of daily Cryptosporidium river concentrations for water resource management: Use of catchment-averaged vs spatially distributed features in a Bagging-XGBoost model. |
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| Authors: | Smalley AL; University of Sheffield, Sheffield S1 3JD, UK. Electronic address: alan.smalley@sheffield.ac.uk., Douterelo I; University of Sheffield, Sheffield S1 3JD, UK. Electronic address: i.douterelo@sheffield.ac.uk., Chipps M; Thames Water Research, Development and Innovation, Kempton Park AWTW, Hanworth TW13 6XH, UK. Electronic address: michael.chipps@thameswater.co.uk., Shucksmith JD; University of Sheffield, Sheffield S1 3JD, UK. Electronic address: j.shucksmith@sheffield.ac.uk. |
| Source: | The Science of the total environment [Sci Total Environ] 2025 Aug 20; Vol. 991, pp. 179794. Date of Electronic Publication: 2025 Jun 20. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1879-1026 |
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| DOI: | 10.1016/j.scitotenv.2025.179794 |
